When It Comes to Social Change, The Machine Metaphor Has Limits

"As communication involves people, and people are unpredictable, attempts to assess programme effects miss their mark when social change is viewed as a predictable, linear process....All the relationships and interactions that make up a functional complex system cannot be known in advance. So development cannot simply involve drawing up a blueprint and then implementing it."

Published in the Communication for Social Change (CFSC) Consortium report Mazi, this paper critiques one way of understanding the CFSC process. According to author Virginia Lacayo, practitioners make a grave error when they assume that the problems that underline the perceived need to promote social change are simple ones. That leads us to plan CFSC initiatives in a linear way, and to design the evaluation of those initiatives with the same parameters. From within this mechanistic perspective, the world (of social change) is a machine which can be operated by planning processes more carefully and with more specifications. Lacayo contends that the result is that "[c]omplex problems and issues probably will act exactly as we predict, and our inputs will result in the outcomes we planned in the expected time frame."

However, she argues, social change is a nonlinear, long-term, and often unpredictable process that requires efforts at multiple levels. Rather than framing approaches in measurable, cause-effect terms (as if programmes can be evaluated in isolation from other efforts and can demonstrate short-term effectiveness), practitioners should envision social change as a complex adaptive system (CAS), in the author's estimation. When planning and/or evaluating initiatives from this perspective, the whole is understood as a product of its parts plus the dynamic relationship between those parts. For instance, an evaluator must look at the entire range of effects triggered by the programme, whether or not they are in line with original intentions. Exceptions, discontinuities, unexpected results, and side effects can offer rich information. A systems inquiry also pays close attention to value (rather than detail), which can be gleaned only be ensuring that the inquiry is relevant to those affected by it. Thus, participation of all possible stakeholders is essential, in Lacayo's eyes. Also needed are approaches that deliberately expose evaluators' and stakeholders' assumptions about what is valid knowledge and that embrace multiple perspectives.

Lacayo concedes that, when we stop viewing parts in isolation, specifying changes in detail, battling resistance to change, reducing variation, and looking for machine-like attributes, we open ourselves to the messiness and unpredictability of complexity - at times arriving at more questions than quick solutions. However, there are some basic guidelines to the CAS approach to social change programming and evaluation, which Lacayo outlines as follows:

1. Replace the "search for best practices" with "facilitating good principles." To support this goal, the evaluation design should be as simple and self-documenting as possible.
2. Adjust monitoring and evaluation approaches to allow for learning from unexpected outcomes, e.g. outcome mapping, rather than retrospectively rationalising that they were intended all along. Incorporate multiple strategies, methodologies, cycle times, dimensions, and informants and triangulate the information obtained from these sources often.
3. Make information about the evaluation process open and accessible to all stakeholders beginning with the design phase.
4. Ask evaluators to adhere to the following 3 rules: Evaluate to inform action; communicate findings to others in terms they care about and understand; and focus on "differences that make a difference."
5. Evaluate and revise the evaluation design often.
6. Make learning the primary outcome, matching the type of evaluation to the maturity level of the system.